scholarly journals A Network Flow-based Analysis of Cognitive Reserve in Normal Ageing and Alzheimer’s Disease

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Sang Wook Yoo ◽  
Cheol E. Han ◽  
Joseph S. Shin ◽  
Sang Won Seo ◽  
Duk L. Na ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
High Education ◽  
Network Flow ◽  
Cognitive Reserve ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Education Levels ◽  
The Brain ◽  
Structural Brain

Abstract Cognitive reserve is the ability to sustain cognitive function even with a certain amount of brain damages. Here we investigate the neural compensation mechanism of cognitive reserve from the perspective of structural brain connectivity. Our goal was to show that normal people with high education levels (i.e., cognitive reserve) maintain abundant pathways connecting any two brain regions, providing better compensation or resilience after brain damage. Accordingly, patients with high education levels show more deterioration in structural brain connectivity than those with low education levels before symptoms of Alzheimer’s disease (AD) become apparent. To test this hypothesis, we use network flow measuring the number of alternative paths between two brain regions in the brain network. The experimental results show that for normal aging, education strengthens network reliability, as measured through flow values, in a subnetwork centered at the supramarginal gyrus. For AD, a subnetwork centered at the left middle frontal gyrus shows a negative correlation between flow and education, which implies more collapse in structural brain connectivity for highly educated patients. We conclude that cognitive reserve may come from the ability of network reorganization to secure the information flow within the brain network, therefore making it more resistant to disease progress.

Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

2021 ◽  
pp. 2250029
Author(s):  
Geng Zhang ◽  
Qi Zhu ◽  
Jing Yang ◽  
Ruting Xu ◽  
Zhiqiang Zhang ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Medical Image Analysis ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Structural Brain Network ◽  
The Brain ◽  
Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

2017 ◽  
Vol 9 (4) ◽  
pp. 46-66 ◽  
Author(s):  
Bhuvaneshwari Bhaskaran ◽  
Kavitha Anandan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Disorder ◽  
Default Mode ◽  
Graph Metrics ◽  
Functional Topology ◽  
The Brain

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

scholarly journals Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8305
Author(s):  
César Covantes-Osuna ◽  
Jhonatan B. López ◽  
Omar Paredes ◽  
Hugo Vélez-Pérez ◽  
Rebeca Romo-Vázquez
Keyword(s):  
Motor Imagery ◽  
Brain Connectivity ◽  
Single Layer ◽  
Network Models ◽  
Brain Regions ◽  
Adaptive Threshold ◽  
Brain Network ◽  
Multilayer Network ◽  
Brain Models ◽  
The Brain

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

scholarly journals Detecting brain connectivity changes during seizures using Granger causality measures on scalp EEG

2015 ◽  
Author(s):  
Christos Koutlis
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Window ◽  
Brain Connectivity ◽  
Characteristic Curve ◽  
Multivariate Time Series ◽  
Brain Regions ◽  
Brain Network ◽  
Network Feature ◽  
The Brain

In this work the objective is to detect brain connectivity changes during epileptic seizures using methods of multivariate time series analysis on scalp multi-channel EEG. Different brain regions represented by the electrode positions interact in terms of Granger causality and these directed connections formulate the brain network at a certain time window. The numerous proposed network features are believed to capture the information of many network characteristics. The ability of a single network feature of the brain network to detect the transition of brain activity from preictal to ictal is examined. The connectivity of the brain is estimated by 13 Granger causality indices on 7 epochs from multivariate time series (19 channels per epoch) at 15 time windows of 20 seconds (5 min in total) before seizure and during the seizure. The characteristics of the networks are estimated by 379 network features. Finally, the discrimination task (preictal vs. ictal) for each network feature is evaluated by the area under receiver operating characteristic curve (AUROC).

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

2021 ◽  
pp. 589-610
Author(s):  
Bhuvaneshwari Bhaskaran ◽  
Kavitha Anandan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Disorder ◽  
Default Mode ◽  
Graph Theoretic ◽  
Functional Topology ◽  
The Brain

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

STRUCTURAL BRAIN NETWORK ABNORMALITIES IN SUBJECTS WITH INTERNET ADDICTION

2017 ◽  
Vol 17 (07) ◽  
pp. 1740031 ◽  
Author(s):  
MIN-HEE LEE ◽  
AREUM MIN ◽  
YOON HO HWANG ◽  
DONG YOUN KIM ◽  
BONG SOO HAN ◽  
...  
Keyword(s):  
Internet Addiction ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Imaging Data ◽  
Structural Brain Network ◽  
Global And Local ◽  
The Impact ◽  
Structural Brain

Although problematic overuse of internet has increased, psychopathological characteristics and neurobiological mechanisms for internet addiction (IA) remain poorly understood. Therefore, it is necessary to investigate the impact of IA on the brain. The present study included 17 subjects with IA and 20 healthy subjects. We constructed the structural brain network from diffusion tensor imaging data and investigated alteration of structural connections in subjects with IA using the network analysis on the global and local levels. The subjects with IA showed increase of regional efficiency (RE) in bilateral orbitofrontal cortex (OFC) and decrease in right middle cingulate and middle temporal gyri ([Formula: see text]), whereas the global properties did not show significant changes. Young’s internet addiction test (IAT) scores and RE in left OFC showed positive correlation, and average time spent on internet per day was positively correlated with the RE in right OFC. This is the first study examining alterations of the structural brain connectivity in IA. We found that subjects with IA showed alterations of RE in some brain regions and RE was positively associated with the severity of IA and average time spent on internet per day. Therefore, RE may be a good property for IA assessment.

scholarly journals Communicability distance reveals hidden patterns of Alzheimer’s disease

2020 ◽  
Vol 4 (4) ◽  
pp. 1007-1029 ◽  
Author(s):  
Eufemia Lella ◽  
Ernesto Estrada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Path Length ◽  
Brain Regions ◽  
Brain Network ◽  
Efficient System ◽  
Brain Hemispheres ◽  
Disconnection Syndrome ◽  
Path Lengths ◽  
The Brain

The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer’s disease. Using this distance, we obtain the shortest communicability path lengths between different regions of brain networks from patients with Alzheimer’s disease (AD) and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach, we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only, and 20% affect vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of AD. Finally, we show that in 76.9% of damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

scholarly journals Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

2020 ◽  
Author(s):  
Lu Wang ◽  
Feng Vankee Lin ◽  
Martin Cole ◽  
Zhengwu Zhang
Keyword(s):  
Oral Reading ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Learning Differences ◽  
Structural Brain Network ◽  
Levels Of Functioning ◽  
The Brain ◽  
Variable Selection Problem ◽  
Structural Brain

AbstractStructural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

scholarly journals Quantifying structural connectivity in brain tumor patients

2021 ◽  
Author(s):  
Yiran Wei ◽  
Chao Li ◽  
Stephen John Price
Keyword(s):  
Brain Tumor ◽  
White Matter ◽  
Robust Estimation ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Mass Effect ◽  
Brain Regions ◽  
Brain Network ◽  
Tumor Patients ◽  
The Brain

AbstractBrain tumors are characterised by infiltration along the white matter tracts, posing significant challenges to precise treatment. Mounting evidence shows that an infiltrating tumor can interfere with the brain network diffusely. Therefore, quantifying structural connectivity has potential to identify tumor invasion and stratify patients more accurately. The tract-based statistics (TBSS) is widely used to measure the white matter integrity. This voxel-wise method, however, cannot directly quantify the connectivity of brain regions. Tractography is a fiber tracking approach, which has been widely used to quantify brain connectivity. However, the performance of tractography on the brain with tumors is complicated by the tumor mass effect. A robust method of quantifying the structural connectivity strength in brain tumor patients is still lacking.Here we propose a method which could provide robust estimation of tract strength for brain tumor patients. Specifically, we firstly construct an unbiased tract template in healthy subjects using tractography. The voxel projection of TBSS is employed to quantify the tract connectivity in patients, using the location of each tract fiber from the template. To further improve the standard TBSS, we propose an approach of iterative projection of tract voxels guided by the tract orientation measured using the voxel-wise eigenvectors. Compared to state-of-the-art tractography, our approach is more sensitive in reflecting more functional relevance. Further, the different extent of network disruption revealed by our approach correspond to the clinical prior knowledge of tumor histology. The proposed method could provide a robust estimation of the structural connectivity for brain tumor patients.

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